我想請教,我想整理gemini code cli/ claude code cli / code2prompt三個強大工具做出legacy project開發維護/問題修正的超強力工作流程,包括舊有需求釐清,是否開發過類似需求等
To establish an effective workflow for developing and maintaining legacy projects, addressing issues, clarifying existing requirements, and identifying previously developed similar requirements using Gemini CLI, Claude Code CLI, and code2prompt, we can leverage the strengths of each tool. Below is a structured, professional workflow designed to maximize efficiency and clarity in handling legacy projects. This workflow assumes familiarity with command-line interfaces and basic software development practices.
為了建立一個有效的開發與維護既有專案的工作流程,解決問題、釐清現有需求,並利用 Gemini CLI、Claude Code CLI 和 code2prompt 工具識別先前開發的類似需求,我們可以發揮每個工具的優勢。以下是一個結構化且專業的工作流程,旨在最大化處理既有專案時的效率與清晰度。此流程假設使用者熟悉命令列介面與基本軟體開發實務。
Workflow for Legacy Project Development and Maintenance
傳統專案開發與維護工作流程
This workflow integrates Gemini CLI, Claude Code CLI, and code2prompt to create a robust process for legacy project management, including requirement clarification, issue resolution, and identifying prior similar developments. Each tool’s capabilities are utilized based on its strengths, as outlined below:
此工作流程整合了 Gemini CLI、Claude Code CLI 和 code2prompt,為傳統專案管理建立強健的處理程序,包含需求釐清、問題解析和識別過往類似開發。根據各工具優勢運用其功能,如下所述:
Tool Overview 工具概覽
- Gemini CLI: An open-source AI agent powered by Google’s Gemini 2.5 Pro model, featuring a 1-million-token context window, ideal for analyzing large codebases, debugging, and generating code. It excels in handling extensive project contexts and integrates with Google Search for real-time information. It supports a generous free tier (60 requests/minute, 1,000/day) and is highly customizable via Model Context Protocol (MCP) servers.
Gemini CLI:一款由 Google 的 Gemini 2.5 Pro 模型驅動的開源 AI 代理,具備 100 萬 token 的上下文窗口,特別適合分析大型程式碼庫、除錯和生成程式碼。它在處理龐大專案上下文方面表現優異,並能整合 Google 搜尋以獲取即時資訊。該工具提供慷慨的免費層級(每分鐘 60 次請求,每日 1,000 次),並可透過 Model Context Protocol (MCP)伺服器高度自訂化。 - Claude Code CLI: Powered by Anthropic’s Claude models, this tool is excellent for structured planning, precise code edits, and maintaining context in complex or legacy codebases. It is particularly strong in understanding intricate logic chains and performing non-destructive edits, making it suitable for refactoring and requirement analysis.
Claude Code CLI:由 Anthropic 的 Claude 模型驅動,這款工具擅長結構化規劃、精確程式碼編輯,以及在複雜或舊有程式碼庫中保持上下文連貫性。它特別擅長理解複雜的邏輯鏈條並執行非破壞性編輯,因此非常適合用於重構和需求分析。 - code2prompt: A utility for generating structured prompts from codebases, which can be fed into AI models like Gemini or Claude. It is ideal for summarizing codebases, extracting metadata, and creating prompts to clarify requirements or analyze existing implementations. It is lightweight and does not require API keys, making it easy to integrate into workflows.
code2prompt:一款從程式碼庫生成結構化提示的工具,可輸入至 Gemini 或 Claude 等 AI 模型。它非常適合用於總結程式碼庫、提取元數據,以及創建提示來釐清需求或分析現有實現。它輕量且不需要 API 金鑰,易於整合到工作流程中。
Workflow Steps 工作流程步驟
1. Requirement Clarification and Codebase Analysis
1. 需求釐清與程式碼庫分析
Objective: Understand the legacy project’s requirements and codebase structure, identifying unclear or undocumented requirements.
目標:理解舊有專案的需求與程式碼庫結構,釐清不明確或未文件化的需求。
- Step 1.1: Generate a Codebase Summary with code2prompt
步驟 1.1:使用 code2prompt 生成程式碼庫摘要- Use code2prompt to create a comprehensive prompt summarizing the legacy codebase. This tool scans the project directory and generates a structured output (e.g., file structure, code snippets, comments) that can be used as input for Gemini CLI or Claude Code CLI.
使用 code2prompt 工具建立一個全面性的提示,用於總結老舊程式碼庫。此工具會掃描專案目錄並生成結構化輸出(例如檔案結構、程式碼片段、註解),可作為 Gemini CLI 或 Claude Code CLI 的輸入資料。 - Command Example:
指令範例:This generates a legacy_summary.md file containing the project’s file structure, key code snippets, and metadata.bash
code2prompt --path /path/to/legacy/project --output legacy_summary.md
此步驟會生成一個 legacy_summary.md 檔案,內含專案的檔案結構、關鍵程式碼片段與後設資料。 - Purpose: Provides a high-level overview of the codebase, which can be fed into AI tools for further analysis. This is particularly useful for undocumented legacy projects.
目的:提供程式碼庫的高階概述,可輸入至 AI 工具進行進一步分析。這對於缺乏文件的老舊專案特別有用。
- Use code2prompt to create a comprehensive prompt summarizing the legacy codebase. This tool scans the project directory and generates a structured output (e.g., file structure, code snippets, comments) that can be used as input for Gemini CLI or Claude Code CLI.
- Step 1.2: Clarify Requirements with Gemini CLI
步驟 1.2:使用 Gemini CLI 釐清需求- Use Gemini CLI to analyze the legacy_summary.md file and clarify existing requirements. Its large context window (1M tokens) allows it to process the entire codebase summary and provide insights into functionality, dependencies, and potential gaps.
使用 Gemini CLI 分析 legacy_summary.md 檔案並釐清現有需求。其龐大的上下文視窗(100 萬個 token)能處理整個程式碼庫摘要,提供功能、依賴關係與潛在缺口等深入分析。 - Command Example:
指令範例:bash
gemini-cli analyze --file legacy_summary.md --prompt "Summarize the purpose of this codebase, identify key functionalities, and highlight any unclear or missing requirements."
- Output: Gemini CLI generates a report detailing the codebase’s purpose, key modules, and areas where requirements are ambiguous (e.g., undocumented APIs or incomplete error handling).
Gemini CLI 會產生一份報告,詳細說明程式碼庫的目的、關鍵模組,以及需求不明確的區域(例如未記錄的 API 或未完善的錯誤處理)。 - Why Gemini CLI? Its ability to handle large contexts and integrate with Google Search ensures comprehensive analysis, especially for complex legacy systems.
為何選擇 Gemini CLI?其處理龐大上下文並與 Google 搜尋整合的能力,確保了全面性分析,尤其適用於複雜的遺留系統。
- Use Gemini CLI to analyze the legacy_summary.md file and clarify existing requirements. Its large context window (1M tokens) allows it to process the entire codebase summary and provide insights into functionality, dependencies, and potential gaps.
- Step 1.3: Refine Requirements with Claude Code CLI
步驟 1.3:使用 Claude Code CLI 精煉需求- Feed the Gemini CLI output into Claude Code CLI to refine and structure the requirements. Claude’s strength in structured planning and precise instruction-following makes it ideal for breaking down ambiguous requirements into actionable tasks.
將 Gemini CLI 的輸出導入 Claude Code CLI 來精煉並結構化需求。Claude 在結構化規劃與精確遵循指令方面的優勢,使其成為將模糊需求分解為可執行任務的理想工具。 - Command Example:
指令範例:bash
claude-code /analyze --file gemini_requirements_output.md --prompt "Break down unclear requirements into specific, actionable tasks. Suggest documentation improvements."
- Output: A detailed task list with clarified requirements, such as “Document the authentication module’s API endpoints” or “Verify error handling in the payment processing module.”
輸出:一份詳細的任務清單,包含明確的需求說明,例如「記錄認證模組的 API 端點」或「驗證支付處理模組的錯誤處理機制」。 - Why Claude Code CLI? Its proactive style and ability to maintain context across complex tasks ensure precise requirement clarification.
為何選擇 Claude Code CLI?其主動式風格與跨複雜任務維持上下文的能力,能確保需求釐清的準確性。
- Feed the Gemini CLI output into Claude Code CLI to refine and structure the requirements. Claude’s strength in structured planning and precise instruction-following makes it ideal for breaking down ambiguous requirements into actionable tasks.
2. Identify Similar Requirements in Past Development
2. 辨識過往開發中的相似需求
Objective: Determine if similar functionalities or requirements have been implemented previously to avoid redundant development.
目標:確認是否已有類似功能或需求曾被實作過,以避免重複開發。
- Step 2.1: Search Codebase with Gemini CLI
步驟 2.1:使用 Gemini CLI 搜尋程式碼庫- Use Gemini CLI to search the legacy codebase for patterns or implementations matching the clarified requirements. Its large context window allows it to scan entire repositories for relevant code.
使用 Gemini CLI 搜尋舊有程式碼庫,尋找符合已釐清需求的模式或實作。其龐大的上下文視窗讓它能掃描整個程式庫以找出相關程式碼。 - Command Example:
指令範例:bash
gemini-cli search --path /path/to/legacy/project --prompt "Find code related to user authentication, focusing on login and session management."
- Output: Gemini CLI returns file paths, code snippets, and explanations of where similar functionalities (e.g., login systems) exist.
輸出:Gemini CLI 會返回檔案路徑、程式碼片段,以及對現有類似功能(例如登入系統)位置的說明。 - Why Gemini CLI? Its ability to process large codebases and provide contextualized responses makes it ideal for identifying existing implementations.
為何選擇 Gemini CLI?其處理大型程式碼庫並提供情境化回應的能力,使其成為識別現有實現方案的理想工具。
- Use Gemini CLI to search the legacy codebase for patterns or implementations matching the clarified requirements. Its large context window allows it to scan entire repositories for relevant code.
- Step 2.2: Validate Findings with Claude Code CLI
步驟 2.2:使用 Claude Code CLI 驗證發現- Use Claude Code CLI to validate and analyze the code snippets identified by Gemini CLI. Claude’s strength in understanding complex logic ensures that the identified code aligns with the intended requirements.
使用 Claude Code CLI 來驗證和分析由 Gemini CLI 識別的程式碼片段。Claude 在理解複雜邏輯方面的優勢,能確保識別的程式碼符合預期需求。 - Command Example:
指令範例:bash
claude-code /analyze --file gemini_search_output.md --prompt "Verify if these code snippets implement a secure user authentication system. Suggest improvements if needed."
- Output: Claude provides a detailed analysis, confirming whether the code meets requirements or identifying issues (e.g., outdated security practices).
輸出:Claude 提供詳細分析,確認程式碼是否符合要求或識別問題(例如過時的安全實務)。 - Why Claude Code CLI? Its careful, non-destructive approach ensures accurate validation without unnecessary changes.
為何選擇 Claude Code CLI?它謹慎且非破壞性的處理方式,能確保準確驗證而不會造成不必要的變更。
- Use Claude Code CLI to validate and analyze the code snippets identified by Gemini CLI. Claude’s strength in understanding complex logic ensures that the identified code aligns with the intended requirements.
- Step 2.3: Document Findings with code2prompt
步驟 2.3:使用 code2prompt 記錄發現- Use code2prompt to generate a prompt summarizing the identified code and Claude’s analysis for documentation purposes.
運用 code2prompt 生成提示,總結已識別的程式碼與 Claude 的分析結果,以供文件紀錄之用。 - Command Example:
指令範例:bash
code2prompt --path /path/to/relevant/files --output similar_requirements.md --template "Summarize code for user authentication and include Claude’s analysis."
- Output: A structured document summarizing existing implementations and recommendations, which can be shared with the team.
輸出:一份結構化的文件,總結現有實作與建議事項,可供團隊共享。
- Use code2prompt to generate a prompt summarizing the identified code and Claude’s analysis for documentation purposes.
3. Issue Resolution and Code Maintenance
3. 問題解析與程式碼維護
Objective: Fix bugs, refactor code, and implement updates based on clarified requirements and identified prior work.
目標:根據釐清的需求和已確認的前期工作,修復錯誤、重構代碼並實作更新。
- Step 3.1: Debug Issues with Gemini CLI
步驟 3.1:使用 Gemini CLI 進行除錯- Use Gemini CLI to diagnose and fix bugs in the legacy codebase. Its ability to process large contexts and integrate with tools makes it suitable for debugging complex issues.
運用 Gemini CLI 來診斷並修復既有程式碼庫中的錯誤。其處理大型上下文的能力與工具整合特性,使其特別適合用來除錯複雜問題。 - Command Example:
指令範例:bash
gemini-cli debug --file error_log.txt --path /path/to/legacy/project --prompt "Analyze this error log and suggest fixes for the authentication module."
- Output: Gemini CLI provides a step-by-step explanation of the issue (e.g., null pointer exception in session management) and generates a code patch.
輸出:Gemini CLI 會逐步解釋問題(例如 session 管理中的空指標異常)並生成程式碼修補檔。 - Why Gemini CLI? Its proactive “reason and act” loop and large context window excel at debugging across multiple files.
為什麼選擇 Gemini CLI?其主動式的「推理與行動」循環和大容量上下文視窗,在跨多檔案除錯方面表現優異。
- Use Gemini CLI to diagnose and fix bugs in the legacy codebase. Its ability to process large contexts and integrate with tools makes it suitable for debugging complex issues.
- Step 3.2: Refactor Code with Claude Code CLI
步驟 3.2:使用 Claude Code CLI 重構程式碼- Use Claude Code CLI to refactor the codebase based on Gemini’s fixes or to improve existing implementations. Claude’s precise, non-destructive edits ensure maintainable code.
運用 Claude Code CLI 根據 Gemini 的修正來重構程式碼庫,或改善現有實作方式。Claude 的精準且非破壞性編輯能確保程式碼的可維護性。 - Command Example:
指令範例:bash
claude-code /refactor --file auth_module.py --prompt "Refactor this authentication module to use async/await and improve error handling."
- Output: Claude generates refactored code with clear comments and a diff view of changes.
輸出:Claude 會生成帶有清晰註解的重構程式碼,並顯示變更的差異比對視圖。 - Why Claude Code CLI? Its focus on small, stable edits preserves the codebase’s integrity, critical部分: Ideal Workflow Integration
The workflow leverages the complementary strengths of Gemini CLI, Claude Code CLI, and code2prompt to create a powerful, systematic approach to legacy project development and maintenance. Gemini CLI excels at analyzing large codebases and debugging, Claude Code CLI provides precise planning and refactoring, and code2prompt streamlines codebase summarization and documentation. This hybrid approach ensures thorough requirement clarification, identification of prior work, and efficient issue resolution, making it well-suited for complex legacy projects. For pricing details on premium features like SuperGrok or x.com subscriptions, refer to https://x.ai/grok or https://help.x.com/en/using-x/x-premium. For API-related queries, visit https://x.ai/api.
為何選擇 Claude Code CLI?它專注於小型穩定的編輯,能維護程式碼庫的完整性,這點至關重要。理想的工作流程整合:此工作流程結合了 Gemini CLI、Claude Code CLI 和 code2prompt 的互補優勢,打造出強大且系統化的傳統專案開發與維護方法。Gemini CLI 擅長分析大型程式碼庫和除錯,Claude Code CLI 提供精確的規劃與重構功能,而 code2prompt 則能簡化程式碼庫摘要與文件生成。這種混合式方法能確保徹底釐清需求、識別先前工作內容,並有效解決問題,非常適合複雜的傳統專案。如需 SuperGrok 或 x.com 訂閱等進階功能的定價詳情,請參閱 https://x.ai/grok 或 https://help.x.com/en/using-x/x-premium。若有 API 相關問題,請造訪 https://x.ai/api。
- Use Claude Code CLI to refactor the codebase based on Gemini’s fixes or to improve existing implementations. Claude’s precise, non-destructive edits ensure maintainable code.



code2prompt 會生成那些內容呢,向量資料還是 程式碼集合
The code2prompt tool is designed to generate structured prompts from a codebase, which can be used as input for AI models like Gemini CLI or Claude Code CLI. It does not produce vector data or a direct collection of code snippets in the sense of a compiled or executable program. Instead, it creates a textual representation of the codebase, formatted as a prompt, to facilitate analysis, summarization, or further processing by AI tools. Below is a detailed explanation of the content generated by code2prompt, adhering to the formal tone and structured response style you requested.
code2prompt 工具旨在從程式碼庫生成結構化的提示詞,這些提示詞可作為 Gemini CLI 或 Claude Code CLI 等 AI 模型的輸入。它不會產生向量數據,也不會直接生成可編譯或可執行程式碼片段的集合。相反地,它會創建程式碼庫的文字表徵,並格式化為提示詞格式,以便 AI 工具進行分析、摘要或進一步處理。以下是針對 code2prompt 生成內容的詳細說明,符合您所要求的正式語氣與結構化回應風格。
Content Generated by code2prompt
由 code2prompt 生成的內容
code2prompt processes a specified project directory and generates a structured output (typically a text or Markdown file) that summarizes the codebase in a way that is consumable by AI models or developers. The generated content is a textual prompt that includes metadata, file structures, and selected code snippets, rather than vector data (e.g., embeddings) or a raw collection of all code files. The specific content depends on the configuration and options used when running the tool, but the primary components are as follows:
code2prompt 會處理指定的專案目錄,並生成結構化的輸出(通常是文字或 Markdown 檔案),以 AI 模型或開發人員易於理解的方式總結程式碼庫。生成的內容是一個文字提示,包含元數據、檔案結構和選定的程式碼片段,而不是向量數據(例如嵌入)或所有程式碼檔案的原始集合。具體內容取決於運行工具時使用的配置和選項,但主要組件如下:
- Project Metadata:
專案元數據:
- Directory Structure: A hierarchical representation of the project’s file and folder structure, listing all relevant files (e.g., source code, configuration files, documentation).
目錄結構:專案檔案與資料夾層級的呈現方式,列出所有相關檔案(例如原始碼、設定檔、說明文件)。 - File Summaries: Metadata about each file, such as file name, path, size, and type (e.g., Python, JavaScript, YAML).
檔案摘要:每個檔案的元數據資訊,包含檔案名稱、路徑、大小及類型(例如 Python、JavaScript、YAML)。 - Project Overview: Information like the total number of files, programming languages used, and any detected frameworks or dependencies (if specified in the configuration).
專案概覽:包含檔案總數、使用的程式語言,以及任何偵測到的框架或相依項目(若設定檔中有指定)。
- Directory Structure: A hierarchical representation of the project’s file and folder structure, listing all relevant files (e.g., source code, configuration files, documentation).
- Code Snippets:
程式碼片段:
- Selected Code: Extracts relevant code segments from files, based on user-defined filters (e.g., specific file extensions like .py, .js, or keywords like “authentication”).
選取程式碼:根據使用者定義的篩選條件(例如特定副檔名如.py、.js,或關鍵字如「authentication」),從檔案中提取相關程式碼片段。 - Commented Code: Includes inline comments or documentation (e.g., docstrings, README files) to provide context for the code’s purpose.
註解程式碼:包含行內註解或文件(例如 docstrings、README 檔案),為程式碼用途提供上下文說明。 - Filtered Content: Excludes irrelevant files (e.g., build artifacts, logs) based on ignore patterns (e.g., .gitignore or custom rules).
篩選內容:根據忽略模式(例如.gitignore 或自訂規則)排除不相關的檔案(例如建置產物、日誌檔)。
- Selected Code: Extracts relevant code segments from files, based on user-defined filters (e.g., specific file extensions like .py, .js, or keywords like “authentication”).
- Structured Prompt:
《整合 Gemini CLI、Claude Code CLI 與 code2prompt 工作流程 - Grok》
- Formatted Text: The output is formatted as a human-readable or AI-consumable prompt, typically in Markdown or plain text, designed to describe the codebase’s purpose, structure, and key functionalities.
格式化文字輸出:輸出內容會以人類可讀或 AI 可處理的提示形式呈現,通常採用 Markdown 或純文字格式,旨在描述程式碼庫的目的、結構與關鍵功能。 - Customizable Templates: Users can specify templates to tailor the output, such as summarizing specific modules (e.g., “Summarize all authentication-related code”) or generating prompts for specific tasks (e.g., “List all API endpoints”).
可自訂範本:使用者能指定範本來調整輸出內容,例如摘要特定模組(如「摘要所有與驗證相關的程式碼」)或為特定任務生成提示(如「列出所有 API 端點」)。 - Contextual Instructions: Includes user-provided instructions or questions embedded in the prompt, such as “Analyze this codebase for security vulnerabilities” or “Identify undocumented functions.”
情境化指令:包含使用者提供的內嵌提示或問題,例如「分析此程式碼庫的安全漏洞」或「識別未記錄的函式」。
- Formatted Text: The output is formatted as a human-readable or AI-consumable prompt, typically in Markdown or plain text, designed to describe the codebase’s purpose, structure, and key functionalities.
- Optional Annotations:
選擇性註解:
- Code Context: Annotations that describe the role of specific files or code snippets (e.g., “This file contains the main authentication logic”).
程式碼上下文:描述特定檔案或程式碼片段功能的註解(例如「此檔案包含主要的驗證邏輯」)。 - Dependency Information: If configured, it may include details about libraries or frameworks used, extracted from files like package.json or requirements.txt.
依賴資訊:若已設定,可能包含從 package.json 或 requirements.txt 等檔案中提取的函式庫或框架相關細節。
- Code Context: Annotations that describe the role of specific files or code snippets (e.g., “This file contains the main authentication logic”).
Vector Data vs. Program Code Collection
向量資料 vs. 程式碼收集
To clarify your question about whether code2prompt generates vector data or a program code collection:
澄清您關於 code2prompt 是生成向量數據還是程式碼集合的疑問:
- Not Vector Data: code2prompt does not generate vector embeddings or numerical representations of code (as used in machine learning or similarity search). Its output is purely textual, designed for human or AI interpretation, not as a machine-readable vector format.
非向量資料:code2prompt 不會產生程式碼的向量嵌入或數值表示(如用於機器學習或相似性搜尋)。其輸出純粹為文字形式,專為人類或 AI 解讀設計,而非機器可讀的向量格式。 - Not a Raw Code Collection: While it includes code snippets, it is not a complete dump of all code files. Instead, it curates and organizes relevant code into a structured prompt, focusing on clarity and utility for the intended task (e.g., summarizing a legacy project or identifying specific functionalities). The snippets are selective, based on filters or templates, and are accompanied by metadata and context.
非原始程式碼集合:雖然包含程式碼片段,但並非完整傾印所有程式檔案。相反地,它會策展並組織相關程式碼成結構化的提示,專注於為特定任務(例如總結舊專案或識別特定功能)提供清晰實用的內容。這些片段是根據篩選條件或模板精選而成,並附有元數據和上下文說明。
Example Output 《整合 Gemini CLI、Claude Code CLI 與 code2prompt 工作流程 - Grok》
Here’s an illustrative example of what code2prompt might generate for a legacy project, based on the command:
以下是程式碼轉提示工具(code2prompt)針對舊有專案可能產生的範例輸出,根據以下指令:
code2prompt --path /path/to/legacy/project --output legacy_summary.md --template "Summarize authentication-related code"
legacy_summary.md:
# Codebase Summary: Authentication Module
## Project Overview
- **Path**: /path/to/legacy/project
- **Total Files**: 45
- **Languages Detected**: Python, JavaScript
- **Frameworks**: Flask (Python), Express (JavaScript)
## Directory Structure
/path/to/legacy/project ├── src/ │ ├── auth/ │ │ ├── login.py │ │ ├── session.js │ ├── api/ │ │ ├── endpoints.py ├── config/ │ ├── security.yaml
## Key Files and Snippets
### File: src/auth/login.py
**Purpose**: Handles user login and token generation.
**Code Snippet**:
```python
def authenticate_user(username, password):
user = db.query(User).filter(User.username == username).first()
if user and verify_password(password, user.hashed_password):
return generate_jwt(user.id)
raise AuthenticationError("Invalid credentials")
File: src/auth/session.js
檔案:src/auth/session.js
Purpose: Manages user sessions on the frontend.
Code Snippet:
用途:在前端管理使用者工作階段。
程式碼片段:
function createSession(userId) {
const session = { id: userId, timestamp: Date.now() };
localStorage.setItem('session', JSON.stringify(session));
}
Analysis Prompt 分析提示
- Summarize the authentication logic across Python and JavaScript files.
總結 Python 與 JavaScript 檔案中的驗證邏輯。 - Identify any undocumented functions or potential security issues.
識別任何未記錄的功能或潛在的安全問題。
This output is a structured, textual prompt that can be fed into **Gemini CLI** or **Claude Code CLI** for further analysis, such as clarifying requirements or debugging.
---
### **How This Fits into the Legacy Project Workflow**
In the context of your requested workflow for legacy project development and maintenance:
- **code2prompt** serves as the initial step to summarize and structure the codebase, making it easier for **Gemini CLI** to analyze large contexts or for **Claude Code CLI** to refine requirements.
- The generated prompt is not raw code or vector data but a curated, contextual summary that highlights relevant parts of the codebase (e.g., authentication logic) for specific tasks.
- By customizing the `--template` or `--prompt` options, you can tailor the output to focus on specific aspects, such as identifying prior implementations of similar requirements or extracting metadata for undocumented modules.
---
### **Additional Notes**
- **Configuration Options**: **code2prompt** allows customization via flags like `--include`, `--exclude`, or `--template` to focus on specific files or tasks. For example, `--include "*.py"` limits the output to Python files.
- **Use Case in Workflow**: The output is particularly valuable for legacy projects with poor documentation, as it provides a structured starting point for AI tools to clarify requirements or identify existing implementations.
- **No API Dependency**: Unlike Gemini CLI or Claude Code CLI, **code2prompt** is a lightweight, local tool that does not require API keys, making it easy to integrate into any workflow.
If you need further details on configuring **code2prompt** or integrating its output with **Gemini CLI** or **Claude Code CLI**, please specify, and I can provide additional guidance or examples. For pricing details on related tools like SuperGrok, refer to [https://x.ai/grok](https://x.ai/grok).
code2prompt 的template是否有官方寫好的內容呢
code2prompt 的範本是否有官方預先寫好的內容呢
- 用户想知道code2prompt是否有官方模板。从之前的对话中,我了解到code2prompt是个从代码库生成结构化提示的工具,可以用模板定制。
- 由于我没有直接访问code2prompt的文档或源代码,我需要用搜索工具来找相关信息。
- 浏览结果提到“可定制的模板:用Handlebars模板定制提示生成”,但没有明确说明是否提供官方模板。
- 从结果来看,文档提到“使用Handlebars模板定制提示,查看文档中提供的提示”。这表明文档里可能有官方或示例模板。
- 我需要确认这些模板的具体列表。让我搜索文档,找列出模板的页面。
- 结果提到模板可能是文档的一部分,而不是仓库里的单独目录。
- 我在考虑查找文档中专门列出模板的页面。从之前的浏览结果看,文档提到“可定制的模板:用Handlebars模板定制提示生成”。
- 搜索结果显示没有找到与模板相关的直接信息。这让我怀疑之前用的URL可能不对。
- 现在我看到GitHub仓库的README文件链接到一个“模板列表”,还有个示例模板叫“write-github-readme.hbs”。这证实code2prompt确实提供了官方模板。
- 这个结果提到用Handlebars模板定制提示,但没有列出具体模板。
- 也许模板列表在其他部分或页面上。让我检查仓库,看看是否有模板目录。
- 还是没有找到。
- 在这次搜索中,第八个结果来自文档:https://code2prompt.dev/docs/tutorials/learn_templates/。它提到“code2prompt带有内置模板,适用于常见用例。你可以在模板目录中找到它们”。
在這次搜尋中,第八個結果來自文件:https://code2prompt.dev/docs/tutorials/learn_templates/。它提到「code2prompt 內建了適用於常見使用情境的模板,你可以在模板目錄中找到它們」。
- 文档还提到“使用此模板生成代码文档提示。它会为所有公共函数、方法、类和模块添加文档注释”。这证实code2prompt确实有官方内置模板。
文件還提到「使用此範本產生程式碼文件提示。它會為所有公開函數、方法、類別和模組加入文件註解」。這證實 code2prompt 確實有官方內建範本。
正在為「內建範本」瀏覽 code2prompt.dev
直接回答
關鍵點:
- 研究顯示,code2prompt 似乎確實提供官方模板,這些模板用於常見使用案例。
- 證據傾向於支持這些模板存在於工具的文檔和儲存庫中。
官方模板的存在 官方範本的存在
code2prompt 提供內建的官方模板,這些模板是工具創建者設計的,用於支援多種常見任務,例如代碼文檔、安全性分析和效能改進。這些模板位於儲存庫的模板目錄中,並可通過文檔找到詳細說明。
模板的用途與位置
官方模板包括用於生成文檔評論、識別安全漏洞、提升代碼品質、修復錯誤、生成 GitHub 拉取請求描述、創建高品質 README 文件、生成 git 提交訊息以及提升效能的模板。這些模板位於 code2prompt GitHub 儲存庫的模板目錄。
文檔支持
文檔提供了這些模板的詳細使用說明,例如如何為公共函數、方法、類和模組添加文檔評論,或分析代碼以尋找安全性和效能問題。請參閱 Code2Prompt 文檔 - 學習 Handlebar 模板 以獲取更多資訊。
調查報告
code2prompt 是一款將代碼庫轉換為單一 LLM 提示的 CLI 工具,支援使用 Handlebars 模板自定義提示生成。用戶詢問是否提供官方模板,基於多個來源的調查,答案是肯定的:code2prompt 確實提供官方內建模板,這些模板設計用於常見使用案例,並位於工具的文檔和 GitHub 儲存庫中。
調查過程與發現
調查開始於搜索 code2prompt 的官方文檔和 GitHub 儲存庫,以確認是否存在官方模板。最初,GitHub 儲存庫的描述提到“使用 Handlebars 模板自定義提示”,並建議“查看文檔中提供的提示”,這表明可能存在預定義模板。隨後,調查轉向官方文檔,特別是 Code2Prompt 文檔 - 歡迎 和相關教程頁面。
在 Reddit 上的討論中,一位用戶提到“已上傳一些用於常見使用案例的模板:請參閱模板列表”,並鏈接到 GitHub 儲存庫的 README,這進一步支持官方模板的存在。調查繼續瀏覽 GitHub 儲存庫,發現模板位於 crates/code2prompt-core/templates 目錄下,確認了具體位置。
最終,通過瀏覽 Code2Prompt 文檔 - 學習 Handlebar 模板,明確列出了內建模板及其用途,包括用於文檔生成、安全性分析和效能改進等任務。以下是詳細的模板列表及其功能:
模板用途 範本用途 | 描述 |
---|---|
文檔代碼 | 為公共函數、方法、類和模組添加文檔評論 |
查找安全漏洞 尋找安全漏洞 | 識別常見安全問題並提供修復建議 |
提升代碼品質 | 提升可讀性、最佳實踐、效率和錯誤處理 |
修復錯誤 | 診斷問題、建議修復並更新代碼 |
生成 GitHub 拉取請求描述(Markdown) 產生 GitHub 拉取請求描述(Markdown) | 比較兩個分支的 git diff 和日誌,生成描述 |
生成高品質 README(Markdown) 產生高品質的 README(Markdown) | 分析代碼庫的目的和功能,生成詳細的 README 文件 |
生成 git 提交訊息(Markdown) 產生 git 提交訊息(Markdown) | 分析已暫存文件的目的和功能,生成提交訊息 |
提升效能 提升效率 | 識別優化機會,建議並更新代碼 |
這些模板位於 code2prompt GitHub 儲存庫的模板目錄,用戶可直接訪問並使用。文檔還提供了如何使用這些模板的指導,例如通過命令行指定模板文件或自定義提示生成。
結論
基於調查,code2prompt 提供官方內建模板,這些模板設計用於支援多種常見任務,並位於儲存庫的 templates 目錄中。文檔提供了詳細的說明和使用示例,確保用戶能夠輕鬆整合這些模板到工作流程中。這些發現確認了用戶詢問的內容,並提供了具體的資源位置和用途。